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Detecting TV Program Highlight Scenes Using Twitter Data Classified by Twitter User Behavior

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Recent Advances and Future Prospects in Knowledge, Information and Creativity Support Systems (KICSS 2015)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 685))

Abstract

This paper presents a novel TV event detection method for automatically generating TV program digests by using Twitter data. Previous studies of TV program digest generation based on Twitter data have developed TV event detection methods that analyze the frequency time series of tweets that users made while watching a given TV program; however, in most of the previous studies, differences in how Twitter is used, e.g., sharing information versus conversing, have not been taken into consideration. Since these different types of Twitter data are lumped together into one category, it is difficult to detect highlight scenes of TV programs and correctly extract their content from the Twitter data. Therefore, this paper presents a highlight scene detection method to automatically generate TV program digests for TV programs based on Twitter data classified by Twitter user behavior. To confirm the effectiveness of the proposed method, experiments using a TV soccer program were conducted.

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Notes

  1. 1.

    https://www.nielsen.com/us/en/press-room/2012/nielsen-and-twitter-establish-social-tv-rating.html.

  2. 2.

    http://live.sportsnavi.yahoo.co.jp/live/soccer/japan/jpn_20131116_01.

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Acknowldgements

This work was supported by JSPS KAKENHI Grant Number 25730210.

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Authors and Affiliations

  1. School of Information and Management Systems Engineering, Nagaoka University of Technology, 1603-1, Kamitomioka Nagaoka, Niigata, 940-2188, Japan

    Tessai Hayama

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  1. Tessai Hayama
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Correspondence to Tessai Hayama .

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Editors and Affiliations

  1. School of Information, Computer, and Communication Technology (ICT), Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand

    Thanaruk Theeramunkong

  2. Department of Automatic Control and Biomedical Engineering, AGH University of Science and Technology, Kraków, Poland

    Andrzej M.J. Skulimowski

  3. Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi-shi, Ishikawa, Japan

    Takaya Yuizono

  4. School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi-shi, Ishikawa, Japan

    Susumu Kunifuji

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Hayama, T. (2018). Detecting TV Program Highlight Scenes Using Twitter Data Classified by Twitter User Behavior. In: Theeramunkong, T., Skulimowski, A., Yuizono, T., Kunifuji, S. (eds) Recent Advances and Future Prospects in Knowledge, Information and Creativity Support Systems. KICSS 2015. Advances in Intelligent Systems and Computing, vol 685. Springer, Cham. https://doi.org/10.1007/978-3-319-70019-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-70019-9_1

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-70019-9

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